
"""simple docstring"""



import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
 import os

 import jax.numpy as jnp
 from jax import jit

 from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
 from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

 a       :						Union[str, Any]       		=		'''0.12'''  # assumed parallelism: 8


if is_torch_available():
 import torch


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A=None   )     ->	int:





    """simple docstring"""

    if rng is None:
        _UpperCAmelCase        = random.Random()

    _UpperCAmelCase        = 1
    for dim in shape:
        total_dims *= dim

    _UpperCAmelCase        = []
    for _ in range(UpperCamelCase_   ):
        values.append(rng.randint(0       ,   vocab_size - 1   )   )

    _UpperCAmelCase        = np.array(UpperCamelCase_       ,   dtype=jnp.intaa   ).reshape(UpperCamelCase_   )

    return output


def 			_UpperCamelCase						(    _A       ,   _A=None   )     ->	List[str]:





    """simple docstring"""

    _UpperCAmelCase        = ids_tensor(UpperCamelCase_       ,   vocab_size=2       ,   rng=UpperCamelCase_   )
    # make sure that at least one token is attended to for each batch
    _UpperCAmelCase        = 1
    return attn_mask




@require_flax
class 		a_    :
    a   :					List[str]			     =					None
    a   :					Optional[Any]			     =					()







    def   _snake_case       (      self      :							Tuple  )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase        = self.model_tester.prepare_config_and_inputs_for_common()

        # cut to half length & take max batch_size 3
        _UpperCAmelCase        = 2
        _UpperCAmelCase        = inputs["""input_ids"""].shape[-1] // 2
        _UpperCAmelCase        = inputs["""input_ids"""][:max_batch_size, :sequence_length]

        _UpperCAmelCase        = jnp.ones_like(lowerCamelCase_  )
        _UpperCAmelCase        = attention_mask[:max_batch_size, :sequence_length]

        # generate max 5 tokens
        _UpperCAmelCase        = input_ids.shape[-1] + 5
        if config.eos_token_id is not None and config.pad_token_id is None:
            # hack to allow generate for models such as GPT2 as is done in `generate()`
            _UpperCAmelCase        = config.eos_token_id
        return config, input_ids, attention_mask, max_length







    @is_pt_flax_cross_test
    def   _snake_case       (      self      :							Dict  )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = self._get_input_ids_and_config()
        _UpperCAmelCase        = False
        _UpperCAmelCase        = max_length
        _UpperCAmelCase        = 0

        for model_class in self.all_generative_model_classes:
            _UpperCAmelCase        = model_class(lowerCamelCase_  )

            _UpperCAmelCase        = model_class.__name__[4:]  # Skip the "Flax" at the beginning
            _UpperCAmelCase        = getattr(lowerCamelCase_  ,       lowerCamelCase_  )
            _UpperCAmelCase        = pt_model_class(lowerCamelCase_  ).eval()
            _UpperCAmelCase        = load_flax_weights_in_pytorch_model(lowerCamelCase_  ,       flax_model.params  )

            _UpperCAmelCase        = flax_model.generate(lowerCamelCase_  ).sequences
            _UpperCAmelCase        = pt_model.generate(torch.tensor(lowerCamelCase_  ,       dtype=torch.long  )  )

            if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
                _UpperCAmelCase        = flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

            self.assertListEqual(pt_generation_outputs.numpy().tolist()  ,       flax_generation_outputs.tolist()  )







    def   _snake_case       (      self      :							List[str]  )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = self._get_input_ids_and_config()
        _UpperCAmelCase        = False
        _UpperCAmelCase        = max_length

        for model_class in self.all_generative_model_classes:
            _UpperCAmelCase        = model_class(lowerCamelCase_  )

            _UpperCAmelCase        = model.generate(lowerCamelCase_  ).sequences
            self.assertEqual(generation_outputs.shape[-1]  ,       lowerCamelCase_  )

            _UpperCAmelCase        = jit(model.generate  )
            _UpperCAmelCase        = jit_generate(lowerCamelCase_  ).sequences

            self.assertListEqual(generation_outputs.tolist()  ,       jit_generation_outputs.tolist()  )







    def   _snake_case       (      self      :							List[Any]  )		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = self._get_input_ids_and_config()
        _UpperCAmelCase        = True
        _UpperCAmelCase        = max_length

        for model_class in self.all_generative_model_classes:
            _UpperCAmelCase        = model_class(lowerCamelCase_  )

            _UpperCAmelCase        = model.generate(lowerCamelCase_  ).sequences
            self.assertEqual(generation_outputs.shape[-1]  ,       lowerCamelCase_  )

            _UpperCAmelCase        = jit(model.generate  )
            _UpperCAmelCase        = jit_generate(lowerCamelCase_  ).sequences

            self.assertListEqual(generation_outputs.tolist()  ,       jit_generation_outputs.tolist()  )







    def   _snake_case       (      self      :							Any  )		->Any:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = self._get_input_ids_and_config()
        _UpperCAmelCase        = False
        _UpperCAmelCase        = max_length
        _UpperCAmelCase        = 2

        for model_class in self.all_generative_model_classes:
            _UpperCAmelCase        = model_class(lowerCamelCase_  )

            _UpperCAmelCase        = model.generate(lowerCamelCase_  ).sequences
            self.assertEqual(generation_outputs.shape[-1]  ,       lowerCamelCase_  )

            _UpperCAmelCase        = jit(model.generate  )
            _UpperCAmelCase        = jit_generate(lowerCamelCase_  ).sequences

            self.assertListEqual(generation_outputs.tolist()  ,       jit_generation_outputs.tolist()  )







    def   _snake_case       (      self      :							int  )		->Any:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = self._get_input_ids_and_config()
        _UpperCAmelCase        = False
        _UpperCAmelCase        = max_length
        _UpperCAmelCase        = 2
        _UpperCAmelCase        = 2

        for model_class in self.all_generative_model_classes:
            _UpperCAmelCase        = model_class(lowerCamelCase_  )

            _UpperCAmelCase        = model.generate(lowerCamelCase_  ).sequences
            self.assertEqual(generation_outputs.shape[0]  ,       input_ids.shape[0] * config.num_return_sequences  )







    def   _snake_case       (      self      :							Union[str, Any]  )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = self._get_input_ids_and_config()
        _UpperCAmelCase        = True
        _UpperCAmelCase        = max_length
        _UpperCAmelCase        = 0.8
        _UpperCAmelCase        = 10
        _UpperCAmelCase        = 0.3
        _UpperCAmelCase        = 1
        _UpperCAmelCase        = 8
        _UpperCAmelCase        = 9

        for model_class in self.all_generative_model_classes:
            _UpperCAmelCase        = model_class(lowerCamelCase_  )

            _UpperCAmelCase        = model.generate(lowerCamelCase_  ).sequences
            self.assertEqual(generation_outputs.shape[-1]  ,       lowerCamelCase_  )

            _UpperCAmelCase        = jit(model.generate  )
            _UpperCAmelCase        = jit_generate(lowerCamelCase_  ).sequences

            self.assertListEqual(generation_outputs.tolist()  ,       jit_generation_outputs.tolist()  )







    def   _snake_case       (      self      :							Optional[int]  )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = self._get_input_ids_and_config()
        _UpperCAmelCase        = max_length
        _UpperCAmelCase        = 1
        _UpperCAmelCase        = 8
        _UpperCAmelCase        = 9

        for model_class in self.all_generative_model_classes:
            _UpperCAmelCase        = model_class(lowerCamelCase_  )

            _UpperCAmelCase        = model.generate(lowerCamelCase_  ).sequences
            self.assertEqual(generation_outputs.shape[-1]  ,       lowerCamelCase_  )

            _UpperCAmelCase        = jit(model.generate  )
            _UpperCAmelCase        = jit_generate(lowerCamelCase_  ).sequences

            self.assertListEqual(generation_outputs.tolist()  ,       jit_generation_outputs.tolist()  )







    def   _snake_case       (      self      :							Any  )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = self._get_input_ids_and_config()
        _UpperCAmelCase        = max_length
        _UpperCAmelCase        = 2
        _UpperCAmelCase        = 1
        _UpperCAmelCase        = 8
        _UpperCAmelCase        = 9

        for model_class in self.all_generative_model_classes:
            _UpperCAmelCase        = model_class(lowerCamelCase_  )

            _UpperCAmelCase        = model.generate(lowerCamelCase_  ).sequences
            self.assertEqual(generation_outputs.shape[-1]  ,       lowerCamelCase_  )

            _UpperCAmelCase        = jit(model.generate  )
            _UpperCAmelCase        = jit_generate(lowerCamelCase_  ).sequences

            self.assertListEqual(generation_outputs.tolist()  ,       jit_generation_outputs.tolist()  )







    def   _snake_case       (      self      :							int  )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = self._get_input_ids_and_config()

        # pad attention mask on the left
        _UpperCAmelCase        = attention_mask.at[(0, 0)].set(0  )

        _UpperCAmelCase        = False
        _UpperCAmelCase        = max_length

        for model_class in self.all_generative_model_classes:
            _UpperCAmelCase        = model_class(lowerCamelCase_  )

            _UpperCAmelCase        = model.generate(lowerCamelCase_  ,       attention_mask=lowerCamelCase_  ).sequences
            self.assertEqual(generation_outputs.shape[-1]  ,       lowerCamelCase_  )

            _UpperCAmelCase        = jit(model.generate  )
            _UpperCAmelCase        = jit_generate(lowerCamelCase_  ,       attention_mask=lowerCamelCase_  ).sequences

            self.assertListEqual(generation_outputs.tolist()  ,       jit_generation_outputs.tolist()  )







    def   _snake_case       (      self      :							Union[str, Any]  )		->Any:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = self._get_input_ids_and_config()

        # pad attention mask on the left
        _UpperCAmelCase        = attention_mask.at[(0, 0)].set(0  )

        _UpperCAmelCase        = True
        _UpperCAmelCase        = max_length

        for model_class in self.all_generative_model_classes:
            _UpperCAmelCase        = model_class(lowerCamelCase_  )

            _UpperCAmelCase        = model.generate(lowerCamelCase_  ,       attention_mask=lowerCamelCase_  ).sequences
            self.assertEqual(generation_outputs.shape[-1]  ,       lowerCamelCase_  )

            _UpperCAmelCase        = jit(model.generate  )
            _UpperCAmelCase        = jit_generate(lowerCamelCase_  ,       attention_mask=lowerCamelCase_  ).sequences

            self.assertListEqual(generation_outputs.tolist()  ,       jit_generation_outputs.tolist()  )







    def   _snake_case       (      self      :							Any  )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = self._get_input_ids_and_config()

        # pad attention mask on the left
        _UpperCAmelCase        = attention_mask.at[(0, 0)].set(0  )

        _UpperCAmelCase        = 2
        _UpperCAmelCase        = max_length

        for model_class in self.all_generative_model_classes:
            _UpperCAmelCase        = model_class(lowerCamelCase_  )

            _UpperCAmelCase        = model.generate(lowerCamelCase_  ,       attention_mask=lowerCamelCase_  ).sequences
            self.assertEqual(generation_outputs.shape[-1]  ,       lowerCamelCase_  )

            _UpperCAmelCase        = jit(model.generate  )
            _UpperCAmelCase        = jit_generate(lowerCamelCase_  ,       attention_mask=lowerCamelCase_  ).sequences

            self.assertListEqual(generation_outputs.tolist()  ,       jit_generation_outputs.tolist()  )







@require_flax
class 		a_    (			unittest.TestCase						):







    def   _snake_case       (      self      :							Union[str, Any]  )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-bert"""  )
        _UpperCAmelCase        = FlaxAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""  )

        _UpperCAmelCase        = """Hello world"""
        _UpperCAmelCase        = tokenizer(lowerCamelCase_  ,       return_tensors="""np"""  ).input_ids

        # typos are quickly detected (the correct argument is `do_sample`)
        with self.assertRaisesRegex(lowerCamelCase_  ,       """do_samples"""  ):
            model.generate(lowerCamelCase_  ,       do_samples=lowerCamelCase_  )

        # arbitrary arguments that will not be used anywhere are also not accepted
        with self.assertRaisesRegex(lowerCamelCase_  ,       """foo"""  ):
            _UpperCAmelCase        = {"""foo""": """bar"""}
            model.generate(lowerCamelCase_  ,       **lowerCamelCase_  )
from __future__ import annotations
class SCREAMING_SNAKE_CASE_		:







   def __init__(     self						:						Dict   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						str				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase			  =      text, pattern
    UpperCamelCase  ,     UpperCamelCase			  =      len(lowerCamelCase_				), len(lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						str				):





    """simple docstring"""


    for i in range(self.patLen - 1   ,   -1   ,   -1				):
     if char == self.pattern[i]:
      return i
    return -1







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						int				):





    """simple docstring"""


    for i in range(self.patLen - 1   ,   -1   ,   -1				):
     if self.pattern[i] != self.text[current_pos + i]:
      return current_pos + i
    return -1







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      []
    for i in range(self.textLen - self.patLen + 1				):
     UpperCamelCase			  =      self.mismatch_in_text(lowerCamelCase_				)
     if mismatch_index == -1:
      positions.append(lowerCamelCase_				)
     else:
      UpperCamelCase			  =      self.match_in_pattern(self.text[mismatch_index]				)
      UpperCamelCase			  =      (
          mismatch_index - match_index
      )  # shifting index lgtm [py/multiple-definition]
    return positions





_SCREAMING_SNAKE_CASE 		=      """ABAABA"""
_SCREAMING_SNAKE_CASE 		=      """AB"""
_SCREAMING_SNAKE_CASE 		=      BoyerMooreSearch(text, pattern)
_SCREAMING_SNAKE_CASE 		=      bms.bad_character_heuristic()

if len(positions) == 0:
     print("""No match found""")
else:
     print("""Pattern found in following positions: """)
     print(positions)
